Research in Engineering Education Symposium 2011 - rees2009

Universidad Politécnica de Madrid (UPM) Página 598 de 957
Figure 2: Tracking skeleton images
There are some important limitations of this representation. There is no knowledge of the
direction being observed by the lecturer, which may be of interest to provide feedback on
user interactions over virtual objects. Additionally, the search for a body-like structure in
the image may lead to extraneous detection artefacts when the user takes real physical
objects, such as the set of papers in the right image of figure 3.
“Actions” may be derived from skeleton movements. PrimeSense provides, in addition to
PrimeSense Sensor drivers, a middleware called NITE performing user identification,
feature detection, and basic gesture recognition using the depth image from the sensor.
There is another software library, called FAAST (described in E. A. Suma et al (2011)),
which interfaces directly with OpenNI/NITE to access this information and performs
additional high-level gesture recognition for generating events based on the user’s actions.
In contrast to articulated skeleton data, actions are more complicated since they require
inferring meaning from the user’s pose and their movements over time. Any of the actions
computed in FAAST (see the complete list of the 36 actions currently defined in
http://projects.ict.usc.edu/mxr/faast/), as well as the basic actions from NITE, can be
bound to virtual keyboard or mouse commands that are sent to the actively selected
window. Furthermore, users can customize the bindings and sensitivity for these actions
at run-time, through the definition of thresholds for action detection, thus providing
flexible input that can easily be adjusted according to the individual user’s body type and
contextual preferences. The initial objective of this library was providing methods to
enable controlling any PC game through Kinect, through the appropriate bindings.
Design of 3D Gestures
The key driver in the design of a new support system for lecturing is the usability of the
system for the lecturer, and the natural integration in the class workflow for students.
Traditionally, as described in J. Nielsen (1993), usability has been associated with a
number of attributes making usability a measurable quantity that can be evaluated
quantitatively and compared:
• Learnability: The use of any new system involves a learning curve. In our case, the
use of natural and meaningful gestures for presentation control is key to increase
learnability, especially as one of the main objectives is deriving virtual object
manipulation procedures.
• Efficiency: A way to quantitatively assess the efficiency is to measure the time it
takes to make a series of specific tasks, for a user who has previously learned to
Proceedings of Research in Engineering Education Symposium 2011
Madrid, 4 th - 7 th October 2011